Adaptive Adjacent Layer Feature Fusion for Object Detection in Remote Sensing Images

Zhang, Xuesong; Gong, Zhihui; Guo, Haitao; Liu, Xiangyun; Ding, Lei; Zhu, Kun; Wang, Jiaqi

doi:10.3390/rs15174224

Cited by 4 publications

(2 citation statements)

References 53 publications

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“…Li et al [31] proposed a cross-layer attention network aiming to obtain the stronger features of small objects for better detection. Zhang et al [32] proposed an adaptive adjacent layer feature fusion (AALFF) method to capture high-level semantic information and accurately locate object spatial positions and improve the adaptability to objects with different sizes.…”

Section: Remote Sensing Image Detectionmentioning

confidence: 99%

MFIL-FCOS: A Multi-Scale Fusion and Interactive Learning Method for 2D Object Detection and Remote Sensing Image Detection

Zhang,

Yu,

Hou

2024

Remote Sensing

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Object detection is dedicated to finding objects in an image and estimate their categories and locations. Recently, object detection algorithms suffer from a loss of semantic information in the deeper feature maps due to the deepening of the backbone network. For example, when using complex backbone networks, existing feature fusion methods cannot fuse information from different layers effectively. In addition, anchor-free object detection methods fail to accurately predict the same object due to the different learning mechanisms of the regression and centrality of the prediction branches. To address the above problem, we propose a multi-scale fusion and interactive learning method for fully convolutional one-stage anchor-free object detection, called MFIL-FCOS. Specifically, we designed a multi-scale fusion module to address the problem of local semantic information loss in high-level feature maps which strengthen the ability of feature extraction by enhancing the local information of low-level features and fusing the rich semantic information of high-level features. Furthermore, we propose an interactive learning module to increase the interactivity and more accurate predictions by generating a centrality-position weight adjustment regression task and a centrality prediction task. Following these strategic improvements, we conduct extensive experiments on the COCO and DIOR datasets, demonstrating its superior capabilities in 2D object detection tasks and remote sensing image detection, even under challenging conditions.

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Section: Remote Sensing Image Detectionmentioning

confidence: 99%

MFIL-FCOS: A Multi-Scale Fusion and Interactive Learning Method for 2D Object Detection and Remote Sensing Image Detection

Zhang,

Yu,

Hou

2024

Remote Sensing

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“…This type of algorithm has high computational complexity but high detection accuracy. One-stage algorithms [27][28][29] perform object detection and localization directly in the image without an explicit candidate box generation step. This approach has the advantages of good real-time performance, simplicity, and efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Edge Aggregation and Multiscale Feature Interaction Detector for Object Detection in Remote Sensing Images

Huang,

Zhao,

Sun

et al. 2023

Remote Sensing

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Object detection (OD) in remote sensing (RS) images is an important task in the field of computer vision. OD techniques have achieved impressive advances in recent years. However, complex background interference, large-scale variations, and dense instances pose significant challenges for OD. These challenges may lead to misalignment between features extracted by OD models and the features of real objects. To address these challenges, we explore a novel single-stage detection framework for the adaptive fusion of multiscale features and propose a novel adaptive edge aggregation and multiscale feature interaction detector (AEAMFI-Det) for OD in RS images. AEAMFI-Det consists of an adaptive edge aggregation (AEA) module, a feature enhancement module (FEM) embedded in a context-aware cross-attention feature pyramid network (2CA-FPN), and a pyramid squeeze attention (PSA) module. The AEA module employs an edge enhancement mechanism to guide the network to learn spatial multiscale nonlocal dependencies and solve the problem of feature misalignment between the network’s focus and the real object. The 2CA-FPN employs level-by-level feature fusion to enhance multiscale feature interactions and effectively mitigate the misalignment between the scales of the extracted features and the scales of real objects. The FEM is designed to capture the local and nonlocal contexts as auxiliary information to enhance the feature representation of information interaction between multiscale features in a cross-attention manner. We introduce the PSA module to establish long-term dependencies between multiscale spaces and channels for better interdependency refinement. Experimental results obtained using the NWPU VHR-10 and DIOR datasets demonstrate the superior performance of AEAMFI-Det in object classification and localization.

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Improving YOLOv7's Remote Sensing Image Object Detection

Huang,

Wang

2023

2023 China Automation Congress (CAC)

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Adaptive Adjacent Layer Feature Fusion for Object Detection in Remote Sensing Images

Cited by 4 publications

References 53 publications

MFIL-FCOS: A Multi-Scale Fusion and Interactive Learning Method for 2D Object Detection and Remote Sensing Image Detection

MFIL-FCOS: A Multi-Scale Fusion and Interactive Learning Method for 2D Object Detection and Remote Sensing Image Detection

A Novel Adaptive Edge Aggregation and Multiscale Feature Interaction Detector for Object Detection in Remote Sensing Images

Improving YOLOv7's Remote Sensing Image Object Detection

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